CAVisAP: Context-Aware Visualization of Outdoor Air Pollution with IoT Platforms

Meruyert Nurgazy (1) , Arkady Zaslavsky (1) , Prem Prakash (2) , Sylvain Kubler (3) , Karan Mitra (4) , Saguna Saguna (4)

1 Deakin University [Burwood]
2 Swinburne University of Technology [Melbourne]
3 CRAN - Centre de Recherche en Automatique de Nancy
4 LUT - Luleå University of Technology = Luleå Tekniska Universitet

Recently air pollution became a severe issue in many big cities due to population growth and rapid development of economy and industry. This led to proliferating need to monitor urban air quality in order to avoid personal exposure as well as to make savvy decisions on managing the environment. In the last decades Internet of Things (IoT) is increasingly being applied to environmental challenges, including air quality monitoring and visualization. In this paper we present CAVisAP, a context-aware system for outdoor air pollution visualization with IoT platforms. The system aims to provide context-aware visualization of three air pollutants such as nitrogen dioxide (NO2), ozone (O3) and particulate matter (PM2.5) in the city of Melbourne, Australia. In addition to primary context as location and time, CAVisAP takes into account users’ pollutant sensitivity levels and color vision impairments to provide personalized pollution maps. Experiments are conducted to validate the system and results are discussed.

Mots clés: context-aware; location-based; data visualization; air pollution; Internet of Things; environmental monitoring

Domaines: Réseaux et télécommunications [cs.NI]

Meruyert Nurgazy, Arkady Zaslavsky, Prem Prakash, Sylvain Kubler, Karan Mitra, et al.. CAVisAP: Context-Aware Visualization of Outdoor Air Pollution with IoT Platforms. 3rd International Workshop on Location-based Services and Applications in Ubiquitous Computing, LSAUC 2019 part of International Conference on High Performance Computing & Simulation, HPCS 2019, Jul 2019, Dublin, Ireland. ⟨hal-02876091⟩